Optical sensor adaptive calibration

ABSTRACT

The subject disclosure provides for a method of optical sensor calibration implemented with neural networks through machine learning to make real-time optical fluid answer product prediction adapt to optical signal variation of synthetic and actual sensor inputs integrated from multiple sources. Downhole real-time fluid analysis can be performed by monitoring the quality of the prediction with each type of input and determining which type of input generalizes better. The processor can bypass the less robust routine and deploy the more robust routine for remainder of the data prediction. Operational sensor data can be incorporated from a particular optical tool over multiple field jobs into an updated calibration when target fluid sample compositions and properties become available. Reconstructed fluid models adapted to prior field job data, in the same geological or geographical area, can maximize the likelihood of quality prediction on future jobs and optimize regional formation sampling and testing applications.

TECHNICAL FIELD

The present description relates in general to downhole measurementsystems, and more particularly to, for example, without limitation,optical sensor adaptive calibration.

BACKGROUND

In the field of oil and gas exploration and production, characterizationof formation or wellbore fluid compositions and properties is importantfor reservoir fluid evaluation and flow assurance analysis. For example,reservoir fluid evaluation deploys formation sampling and testingtechniques to collect fluid samples with minimized contamination, andfurther facilitate early decision making on the economic value ofpotential reservoir exploration, well completion and production based onthe quality prediction of the fluid compositions and properties. Flowassurance analysis may require determination of the quality of flowmovement, or the condition of a pipeline through deposit (such asasphaltenes) or erosion evaluation, or to adjust and modify a drillingor production parameter, and optimize damage preventive design.

Common practice may use fluid answer product predictive models forreal-time downhole fluid analysis during formation fluid sampling andtesting. Real-time formation fluid analysis using fluid characterizationmodels with synthetic optical sensor inputs is sensitive to the qualityof sensor data transformation from the downhole tool parameter space tothe synthetic parameter space, and to the quality of multivariate inputselection. In common practice, each sensor has its own sensor-basedfluid characterization models and cross-space data transformationmodels. While fluid characterization models are calibrated in asynthetic database using virtual sensor responses on a large collectionof global oil and fluid samples with known properties, cross-space datatransformation models are usually trained on a small number of referencefluids with measured sensor responses as calibration inputs andsimulated virtual sensor responses as calibration outputs.

Traditional optical sensor calibration methods for fluid answer productprediction are based on synthetic sensor data, and the quality ofreal-time field data prediction depends on the uncertainty of thecalibration data, the robustness of resulting models, and thereliability of reconstructed synthetic sensor data from fieldoperational sensor responses. The models built on pure synthetic sensordata alone may suffer unpleasant variation in prediction if calibrationdata is unrealistic or field sample compositions and properties are outof calibration data range. The issue may also arise if optical sensordata transformation/standardization algorithms built on the certainreference fluids fail to generalize the well on the new data and/or totolerate unexpected raw sensor signal change over time, makingre-constructed synthetic sensor data questionable.

BRIEF DESCRIPTION OF THE DRAWINGS

The following figures are included to illustrate certain aspects of thepresent disclosure, and should not be viewed as exclusive embodiments.The subject matter disclosed is capable of considerable modifications,alterations, combinations, and equivalents in form and function, withoutdeparting from the scope of this disclosure.

FIG. 1 illustrates a calibration system used to calibrate an opticalsensor in accordance with one or more implementations of the subjecttechnology.

FIG. 2A illustrates a waveform depicting an example of a dynamic rangeof actual sensor responses of each channel on reference fluids inaccordance with one or more implementations of the subject technology.

FIG. 2B illustrates a waveform depicting an example of a dynamic rangeof synthetic sensor responses of each channel on reference fluids inaccordance with one or more implementations of the subject technology.

FIG. 3A illustrates a waveform depicting an example of a normalizedscale of actual sensor responses of each channel on reference fluids inaccordance with one or more implementations of the subject technology.

FIG. 3B illustrates a waveform depicting an example of a normalizedscale of synthetic sensor responses of each channel on reference fluidsin accordance with one or more implementations of the subjecttechnology.

FIG. 4 illustrates a flowchart of a process for optical sensor adaptivecalibration in accordance with one or more implementations of thesubject technology.

FIG. 5 illustrates a flowchart diagram of an example of a process forperforming optical sensor adaptive calibration in accordance with one ormore implementations of the subject technology.

FIG. 6 illustrates a waveform depicting exemplary inputs for the opticalsensor adaptive calibration of FIG. 5 in accordance with one or moreimplementations of the subject technology.

FIG. 7 illustrates a flowchart diagram of an example of process foroptimizing real-time data processing in accordance with one or moreimplementations of the subject technology.

FIGS. 8A to 8D illustrate waveforms depicting respective comparisons ofresults of a real-world data prediction example from direct mappingwithout using optical data transformation and the results of predictionthrough optical data transformation in accordance with one or moreimplementations of the subject technology.

FIG. 9 illustrates a flowchart diagram of an example of process forperforming sensor-and-field-based neural network calibration adapted toboth actual and synthetic sensor responses in accordance with one ormore implementations of the subject technology.

FIG. 10 illustrates a general transformation model framework applied toa forward transformation and a reverse transformation between a toolparameter space and a synthetic parameter space with neural networks inaccordance with one or more implementations of the subject technology.

FIG. 11A illustrates a schematic view of a logging operation deployed inand around a well system in accordance with one or more implementationsof the subject technology.

FIG. 11B illustrates a schematic view of a wireline logging operationdeployed in and around a well system in accordance with one or moreimplementations of the subject technology.

FIG. 11C illustrates a schematic view of a well system that includes thelogging tool in a logging while drilling (LWD) environment in accordancewith one or more implementations of the subject technology.

FIG. 12 is a block diagram illustrating an example computer system withwhich the computing subsystem of FIG. 11A can be implemented.

In one or more implementations, not all of the depicted components ineach figure may be required, and one or more implementations may includeadditional components not shown in a figure. Variations in thearrangement and type of the components may be made without departingfrom the scope of the subject disclosure. Additional components,different components, or fewer components may be utilized within thescope of the subject disclosure.

DETAILED DESCRIPTION

After manufacture and before downhole use, each optical computing deviceis carefully calibrated against known reference fluids for temperatureand pressure ranges expected to be encountered in the field. Themeasurement data of each sensing element on the given reference fluidsform the basis for developing optical signal transformation models. Onceselected reference fluids adequately possess representative features ofglobal petroleum and/or formation fluids, the optical signaltransformation algorithms calibrated with a variety of structures can befound for a wide range of applications in processing downhole opticaltool data.

Formation fluid analysis uses field sensor measurements obtained fromdownhole fluid sampling. Accordingly, factors that may have strongimpact on the quality of fluid prediction (e.g., fluid composition andfluid characteristics) include variations in downhole fluid pumpingrate, a transient status in the flow line, tool vibrations, firmwarechanges, the condition of sensing elements and optical systemcomponents, fluid contamination level, and other testing conditions.Optical sensor adaptive calibration as disclosed herein provides arobust real-time fluid prediction with respect to the above factors.This may be particularly desirable when a new sensor is deployed for thefirst time.

The subject disclosure provides for an improved method for opticalsensor calibration to overcome the limitation above through adaptingpotential optical signal vibration of both synthetic and actual sensorinputs integrated from multiple sources. For example, the subjectdisclosure provides for a novel adaptive calibration method to makereal-time optical fluid answer product prediction less dependent to thesparse data based sensor signal transformation. The fluid predictivemodels calibrated with the adaptive calibration of the subjecttechnology are compatible with each type of inputs, making in-situsignal processing workflow switching possible by using eitheralternative type of sensor inputs or ruggedized real-time downhole fluidanalysis.

The adaptive calibration method of the subject technology is implementedwith neural networks through enhanced machine learning, which has thepower to accurately fit the training data from various measurement andsimulation sources, and meanwhile generalize well on the unseen newdata. For sensor-based fluid model calibration, conventional syntheticsensor data is combined with measured actual sensor data through a novelnormalization scheme and create a new pooled calibration environment.The subject disclosure also provides for the incorporation ofoperational sensor data from a particular optical tool over multiplefield jobs into an updated calibration when target fluid samplecompositions and properties become available. The re-constructed fluidmodels adapted to previous field job data, especially in the samegeological or geographical area, would maximize the likelihood ofquality prediction on the future jobs and optimize regional formationsampling and testing applications.

Since fluid answer product predictive models calibrated with theadaptive calibration of the subject technology are compatible with bothsynthetic and actual sensor inputs, downhole real-time fluid analysiscan then be performed by monitoring the quality of the prediction witheach type of inputs and determining which type of inputs generalizesbetter. The processor can then bypass the less robust routine and keepthe better one for rest of data prediction. This approach overcomes thecurrent limitation, which only relies on transformed optical sensor datafor real-time fluid analysis, and therefore improves the service qualityof formation sampling and testing.

Using available lab and quality control results on previous jobs tomodify the model calibration applied to new jobs would help regionalhistorical fluid data interpretation. The job relevant sensor data canbe used alone or integrated with generic calibration data for pooleddata calibration. The target inclusive information will help maximizethe chance of quality data prediction for the sensor deployed in futurejobs. This approach is particularly suitable to the sensors or toolsdeployed in the same geological/geographical area, and capable ofcompensating for optical signal variations over time in both syntheticand actual sensor spaces.

The subject technology provides several advantages over traditionalcalibration methods, such as 1) maximizing the likelihood of qualityprediction by making calibration less dependent to a single type ofinputs, 2) overcoming the limitation associated with reference fluidselection and reverse transformation, 3) implementing adaptivecalibration in pooled sensor spaces with neural network based deeplearning, and 4) providing an alternative workflow for quality assurancecontrol of fluid model prediction during real-time data processing.

Optical sensor adaptive calibration as disclosed herein may includepools of optical sensors having the same or similar multi-elementconfiguration, and sharing at least one integrated computation element(ICE) having the same or similar design. Without limitation, some of theICE designs used as sensing elements in optical sensors as disclosedherein may include a methane ICE (designed to measure methaneconcentration), a gas-oil-ratio (GOR) ICE (designed to measure GOR in afluid), or an aromatics (ARO) ICE (designed to measure AROconcentration), among others.

An ICE as disclosed herein is a processing element that opticallyinteracts with a substance to determine quantitative and/or qualitativevalues of one or more physical or chemical properties of a substance tobe analyzed. An ICE may include a plurality of optical layers consistingof various materials whose index of refraction and size (e.g.,thickness) may vary between each layer. The ICE may comprise amultilayered interference element designed to operate over a continuumof wavelengths in the electromagnetic spectrum from the ultraviolet (UV,about 290 nm to about 400 nm), through the visible (VIS, about 400 nm toabout 750 nm), through the near-infrared (NIR, about 750 nm to about2500 nm), and to mid-infrared ranges (MIR, about 2500 nm to about 10,000nm), or any sub-set of that region. An ICE design refers to the numberand thickness of the respective layers of the ICE. The layers may bestrategically deposited and sized to selectively pass predeterminedfractions of electromagnetic radiation at different wavelengthsconfigured to substantially mimic a regression vector corresponding to aparticular physical or chemical property of interest of a substance.Accordingly, an ICE design will exhibit a transmission function that isweighted with respect to wavelength. As a result, the output lightintensity from the ICE conveyed to a detector may be related to thephysical or chemical property of interest for the substance. Forexample, electromagnetic radiation that optically interacts with the ICEis modified to be readable by a detector such that an output of thedetector can be correlated to the physical or chemical property or“characteristic” of the substance being analyzed.

As used herein, the term “characteristic” refers to a chemical,mechanical, or physical property of a substance. The characteristic ofthe substance may include a quantitative or qualitative value of one ormore chemical constituents or compounds present therein, or any physicalproperty associated therewith. Such chemical constituents and compoundsmay be alternately referred to as “analytes.” Illustrativecharacteristics of a substance that can be monitored with opticalcomputing devices described herein can include chemical composition(e.g., identity and concentration in total or of individual components),phase presence (e.g., gas, oil, water, etc.), impurity content, ioncontent, pH, alkalinity, viscosity, density, ionic strength, totaldissolved solids, salt content (e.g., salinity), porosity, opacity,bacteria content, total hardness, combinations thereof, state of matter(solid, liquid, gas, emulsion, mixtures, etc.), and the like.

As used herein, the term “optical computing device” refers to an opticaldevice that is configured to receive an input of electromagneticradiation, to interact the electromagnetic radiation with a substanceand to produce an output of electromagnetic radiation from a processingelement arranged within the optical computing device. In someimplementations, an optical computing device also includes a detector togenerate an electronic signal indicative of a characteristic of thesubstance. The processing element may be, for example, an ICE, or amultivariate optical element (MOE). The electromagnetic radiation thatoptically interacts with the processing element is modified to bereadable by a detector, such that an output of the detector can becorrelated to a particular characteristic of the substance. The outputof electromagnetic radiation from the processing element can bereflected, transmitted, and/or dispersed electromagnetic radiation.Whether the detector analyzes reflected, transmitted, or dispersedelectromagnetic radiation may be dictated by the structural parametersof the optical computing device as well as other considerations known tothose skilled in the art. In addition, emission and/or scattering of thefluid, for example via fluorescence, luminescence, phosphorescence,scintillation, incandescence, Raman, Mie, and/or Raleigh scattering, canalso be monitored by optical computing devices.

An optical sensor as disclosed herein may include at least one or moresensing elements. In some implementations, at least one of the sensingelements is an ICE designed to measure a fluid characteristic orproperty. According to some implementations, an ICE is essentially anoptical interference-based device that can be designed to operate over acontinuum of wavelengths in the electromagnetic spectrum from the UV tomid-infrared (MIR) ranges, or any sub-set of that region.Electromagnetic radiation that optically interacts with a substance ischanged and processed by the ICE to be readable by a detector, such thatan output of the detector can be correlated to the physical or chemicalproperty of the substance being analyzed. Other examples of sensingelements and optical system components may include band-pass filters,notch filters, neutral density filters, beam-splitters, polarizingbeam-splitters, prisms, diffraction gratings, Fresnel lenses, and thelike.

As used herein, the term “optically interact” or variations thereofrefers to the reflection, transmission, scattering, diffraction, orabsorption of electromagnetic radiation either on, through or from oneor more processing elements (i.e., ICE or MOE components) or a substancebeing analyzed by the processing elements. Accordingly, opticallyinteracted light refers to electromagnetic radiation that has beenreflected, transmitted, scattered, diffracted, or absorbed, emitted, orre-radiated, for example, using a processing element, but may also applyto interaction with a substance.

As used herein, the term “electromagnetic radiation” refers to radiowaves, microwave radiation, any radiation in the UV, VIS, NIR or MIRregions, X-ray radiation and gamma ray radiation.

The terms “optical computing device” and “optical sensor” may be usedherein interchangeably and refer generally to a sensor configured toreceive an input of electromagnetic radiation that has interacted with asubstance and produced an output of electromagnetic radiation from asensing element arranged within or otherwise forming part of the opticalcomputing device. The sensing element may be, for example, an ICE asdescribed above. Prior to field use, the optical computing device, witheach sensing element employed therein, is calibrated such that eachoutput response can be used in conjunction with others to calculatefluid composition and properties through various signal transformationand characterization models upon being exposed to downhole conditions.When an optical computing device is not properly calibrated, theresulting models or algorithms, which correlate optical sensor responsesto the fluid characteristics of interest, may not be able to provideaccurate fluid predictions upon deployment.

FIG. 1 illustrates an exemplary calibration system 100 that may be usedto calibrate one or more sensing elements used in an optical sensor. Asillustrated, system 100 may include a measurement system 102 in opticalcommunication with one or more sensing elements 104 (shown as 104 a, 104b, 104 c . . . 104 n) that are to be calibrated. Each sensing element104 a-n may include, without limitation, an optical band-pass filter ora multivariate sensing element/integrated computational element (e.g.,an ICE). Measurement system 102 may circulate one or more referencefluids with different chemical compositions and properties (i.e.,methane concentration, aromatics concentration, saturates concentration,GOR, and the like) through an optic cell 106 over widely varyingcalibration conditions of temperature, pressure, and density. Thus,optical transmission and/or reflection measurements of each referencefluid in conjunction with each sensing elements 104 a-n may be made atsuch conditions.

Measurement system 102 may comprise an opticalpressure-volume-temperature (PVT) instrument, and the reference fluidscirculated in the measurement system 102 may include representativefluids commonly encountered in downhole applications. System 100 maycollect output signals from each sensing element 104 a-n for eachspecified reference fluid at varying calibration conditions. In somecases, the reference fluids may include representative fluids that areeasy to operate for manufacturing calibration such as: dodecane,nitrogen, water, toluene, 1-5 pentanediol, and two liquid crude oils orfluids with no gas concentration (e.g., dead oil). The crude reservoiroils used as reference fluids may be, for example, global oil library 13(or “GOL13”), and global oil library 33 (or “GOL33”). In other cases,the reference fluids may include samples of live oils mixed with deadoil and hydrocarbon gas, e.g., methane, and the samples of hydrocarbongases and/or CO₂.

Measurement system 102 may vary each reference fluid over several setpoints spanning varying calibration conditions. To accomplish this, asillustrated, measurement system 102 may include a liquid charging system108, a gas charging system 110, a temperature control system 112, and apressure control system 114. The liquid charging system 108 injectsreference fluids into the fluid circuit to introduce fluid varyingperturbations such that calibrating the sensing elements 104 a-n willincorporate all the expected compounds found in the particular referencefluid. The gas charging system 110 may inject known gases (e.g., N₂.CO₂, H₂S, methane, propane, ethane, butane, combinations thereof, andthe like) into the circulating reference fluids. The temperature controlsystem 112 may vary the temperature of the reference fluid to simulateseveral temperature set points that the sensing elements 104 a-n mayencounter downhole. Lastly, the pressure control system 114 may vary thepressure of the reference fluid to simulate several pressure set pointsthat the sensing elements 104 a-n may encounter downhole.

Optic cell 106 is fluidly coupled to each system 108, 110, 112, and 114to allow the reference fluids to flow therethrough and recirculate backto each of the systems 108, 110, 112, and 114 in a continuous,closed-loop fluid circuit. While the reference fluid circulates throughoptic cell 106, a light source 116 emits electromagnetic radiation 118that passes through optic cell 106 and the reference fluid flowingtherethrough. As the electromagnetic radiation 118 passes through theoptic cell 106 it optically interacts with the reference fluid andgenerates sample interacted light 120, which includes spectral data forthe particular reference fluid circulating through the measurementsystem 102 at the given calibration conditions or set points. The sampleinteracted light 120 may be directed toward sensing elements 104 a-n,which, as illustrated, may be arranged or otherwise disposed on asensing platform 122. Sensing elements 104 a-n receive sample interactedlight 120 and generate a computation light 124 that is measured by adetector 126.

Sensing platform 122 is configured to provide at least a portion ofsample interacted light 120 having similar optical properties to each ofthe plurality of sensing elements 104 a-n. In some implementations,sensing platform 122 provides the same portion of sample interactedlight 120 to the plurality of sensing elements 104 a-n in a known timesequence. In some implementations, sensing platform 122 includes asensor wheel configured to rotate in the direction A, about an axisparallel to the impinging sample interacted light 120. While shown asarranged in a single ring on sensing platform 122, sensing elements 104a-n may alternatively be arranged in two or more rings on the sensingplatform 122. Once calibrated, according to implementations disclosedherein, sensing elements 104 a-n mounted on sensing platform 122 may beincluded in a downhole tool for measurement of a fluid characteristic.

During calibration, sensing platform 122 may be rotated at apredetermined frequency such that each sensing element 104 a-n mayoptically interact with the sample interacted light 120 for a briefperiod and sequentially produce optically interacted light 124 that isconveyed to detector 126. Detector 126 may be generally characterized asan optical transducer and may comprise, but is not limited to, a thermaldetector (e.g., a thermopile), a photo-acoustic detector, asemiconductor detector, a piezo-electric detector, a charge coupleddevice (CCD) detector, a video or array detector, a split detector, aphoton detector (e.g., a photomultiplier tube), photodiodes, and anycombination thereof. Upon receiving individually-detected beams ofcomputation light 124 from each sensing element 104 a-n, detector 126may generate or otherwise convey corresponding response signals 128 to adata acquisition system 130. A data acquisition system 130 may timemultiplex each response signal 128 received from the detector 126corresponding to each sensing element 104 a-n. A corresponding set ofresulting output signals 132 is generated and conveyed to a fluidanalysis device 134, for processing and providing input parameters forvarious fluid predictive models. The fluid predictive models use outputsfrom each sensing element 104 a-n as candidate variables.

In some implementations, the fluid analysis device 134 may be coupled toa computer 140, which may include a memory 142 and a processor 144.Memory 142 may store commands which, when executed by processor 144,cause computer 140 to perform at least some of the steps in the methodsdescribed herein and otherwise consistent with the present disclosure.For example, in implementations consistent with the present disclosure,models and algorithms for data processing and fluid computation modelsas disclosed herein may be implemented into processor 144.

Once sensing platform 122 is calibrated, one or more calibrated sensingplatforms 122 may then be installed on a downhole tool with other systemcomponents, for assembly validation testing. To validate the opticalresponse of the sensor assembly, the sensor may be placed in an oventhat regulates the ambient temperature and pressure. The referencefluids used to calibrate sensing platform 122 may be selectivelycirculated through the optical sensor at similar set points used tocalibrate the sensing elements 104 a-n. More particularly, the referencefluids may be circulated through the optical sensor at various set pointdownhole conditions (i.e., elevated pressures and temperatures) toobtain measured optical responses.

Sensing elements 104 a-n are calibrated using the response of thesensors to reference fluids in a tool parameter space. On the otherhand, fluid spectroscopic analysis and fluid predictive modelcalibration using a large amount of data in a standard oil library isperformed in a synthetic parameter space. Synthetic sensor responses foreach sensor in the downhole tool are calculated as a dot product offull-wavelength-range of fluid spectrometry and sensor element spectrumexcited by a light source. The value of the dot product may varynonlinearly or linearly compared to the actual sensor response due tothe difference between the mathematical approximation used incalculating synthetic sensor response and the real systemimplementation. To compensate for the difference above, the measurementdata from the sensors in the downhole tool can be transformed from thetool parameter space to the synthetic parameter space through a reversetransformation algorithm before applying fluid predictive models. Insome implementations, fluid predictive models are calibrated withdifferent synthetic optical inputs, and saved as candidate models in anoptical fluid model base.

In current practice, an optical fluid model is dependent on the downholetool used for measurement and includes data transformation (i.e.,standardization) models and property predictive models. To provideadequate flexibility for optical data processing and interpretation, anoptical fluid model includes the following candidate constituents:transformation models calibrated on selected reference fluids throughreverse transformation, transformation models calibrated on selectedreference fluids through forward transformation, and predictive modelscalibrated on both Optical-PVT database and sensing platform 122 dataspaces. Depending on the data space in which the fluid propertypredictive models are calibrated, data transformation models convertmeasured or simulated optical sensor output between a tool parameterspace and a synthetic parameter space. FIG. 10 illustrates one suchtransformation.

The synthetic sensor data that is synthesized from Optical-PVT fluidspectroscopies, the measured sensor wheel transmittance spectra, andapproximated transfer function of optical train system, is traditionallyused alone with corresponding fluid composition and property data (knownas target characteristics) to calibrate fluid answer product predictivemodels for real-time downhole fluid analysis during formation fluidsampling and testing. The synthetic sensor database usually contains alarge number of oil and gas samples collected globally which representtypical fluid information of heavy oil, medium oil, volatile and lightoil, oil and gas condensates, wet and dry gas in different geographicalregions. The optical responses simulated in synthetic sensor space isdifferent from actual sensor responses mainly because of the variationor uncertainty of transfer function of optical train system convolvedover the firmware components such as light source, fluid cell/window,calcium fluoride (CaF2), and signal detector after the tool is built.

To apply the fluid answer product predictive models calibrated insynthetic parameter space, the current practice relies on atransformation or signal standardization algorithm that converts theactual sensor responses to synthetic sensor responses through anonlinear mapping with neural network, for example. The transformationalgorithm is typically calibrated on the selected reference fluids withboth measured actual sensor responses in manufacturing calibration andsimulated synthetic sensor responses at the same temperature andpressure setting points available. However, only a small number offluids can be selected as reference fluids for transformation algorithmdevelopment on each sensor. In this respect, the quality transformation,which demands use of more reference fluids to achieve unbiased mapping,therefore becomes a challenging problem for real-time downhole opticalfluid analysis. In addition, the change on reference fluids selectiondue to its availability in stock may also alter the training datadistribution in calibrating transformation algorithm that could affectthe data mapping during signal processing.

FIG. 2A illustrates a waveform 200 depicting an example of a dynamicrange of actual sensor responses of each channel on reference fluids inaccordance with one or more implementations. FIG. 2B illustrates awaveform 250 depicting an example of a dynamic range of synthetic sensorresponses of each channel on reference fluids in accordance with one ormore implementations.

FIGS. 2A and 2B illustrate the dynamic range of optical signal responseson a number of reference fluids obtained from an actual sensor and asynthetic sensor respectively. The sensor consists of 33 effectivechannels or components realized with different ICEs to measure variousanalyte-specific fluid compositions and properties. Although themeasured actual sensor responses (e.g., 200) and simulated syntheticsensor responses (e.g., 250) in FIGS. 2A and 2B, respectively, aresignificantly different in initial scale on some data channels, they canbe demonstrated in close similarity in normalized scale with dynamicrange on each channel varied from −1 to +1 as shown in FIGS. 3A and 3B.

FIG. 3A illustrates a waveform 300 depicting an example of a normalizedscale of actual sensor responses of each channel on reference fluids inaccordance with one or more implementations. FIG. 3B illustrates awaveform 350 depicting an example of a normalized scale of syntheticsensor responses of each channel on reference fluids in accordance withone or more implementations.

The identified feature from normalized data evaluation motivatesdevelopment of an improved calibration scheme by using merged data,which can adapt the optical signal variation in both synthetic andactual sensor spaces with more complex neural network structures. It isexpected that reliability of real-time optical fluid analysis would beimproved with implementation of the adaptive calibration even if thesynthetic sensor data transformed from raw optical data fails to workproperly as validated model inputs due to large mapping error. This isbenefited from adaptively calibrated fluid models that can alternativelytake actual sensor responses as inputs directly to generate substitutedoutputs in answer product prediction for early decision making duringformation fluid sampling and testing.

The subject disclosure provides for an improved method for opticalsensor calibration to overcome the limitation above through adaptingpotential optical signal vibration of both synthetic and actual sensorinputs integrated from multiple sources. For example, the subjectdisclosure provides for a novel adaptive calibration method to makereal-time optical fluid answer product prediction less dependent to thesparse data based sensor signal transformation. The fluid predictivemodels calibrated with the adaptive calibration of the subjecttechnology are compatible with each type of inputs, making in-situsignal processing workflow switching possible by using eitheralternative type of sensor inputs or ruggedized real-time downhole fluidanalysis.

FIG. 4 illustrates a flowchart of a process 400 for optical sensoradaptive calibration in accordance with one or more implementations ofthe subject technology. Further for explanatory purposes, the blocks ofthe sequential process 400 are described herein as occurring in serial,or linearly. However, multiple blocks of the process 400 may occur inparallel. In addition, the blocks of the process 400 need not beperformed in the order shown and/or one or more of the blocks of theprocess 400 need not be performed.

The process 400 starts at step 402, where an optical tool is deployedinto a wellbore penetrating a subterranean formation. Next, at step 404,field measurements are obtained with the deployed optical tool.Subsequently, at step 406, an adaptive fluid predictive model calibratedwith a plurality of types of sensor data is determined. In some aspects,the plurality of types of sensor responses include a first type ofsensor response associated with a synthetic parameter space and a secondtype of sensor response associated with a tool parameter space. Next, atstep 408, the adaptive fluid predictive model is applied, in a processorcircuit, to one or more fluid samples from the obtained fieldmeasurements. Subsequently, at step 410, a value of a fluid answerproduct prediction is determined with the applied adaptive fluidpredictive model. Next, at step 412, a fluid answer signal indicatingthe value of the fluid answer product prediction is provided forfacilitating downhole fluid sampling with a wellbore operation.

In some implementations, a fluid answer product or “fluid answer signal”may be generated and otherwise derived from the various adaptive fluidpredictive model responses discussed herein. The fluid answer signal,for example, may be computed and generated using the computing subsystem1110 (see FIG. 11) of the surface equipment 1112 (see FIG. 11), or withany other computing device or facility with access to the adaptive fluidpredictive model responses. The fluid answer signal values may beprovided on a graphical user interface or any other format capable ofdisplaying or providing fluid answer product prediction values forconsideration. In some implementations, the fluid answer signal mayinclude and graphically display evaluation results taken from some orall of the logging tool 1102 (see FIG. 11). In at least oneimplementation, the fluid answer signal may further include a compositelog derived from field measurements obtained from the logging tool 1102.The fluid answer signal may also include interpretation highlights thatidentify intervals of interest, historical results, and possiblerecommendations on proceeding, such as preferred locations to performdownhole fluid sampling. In some implementations, the fluid answersignal may further include an interpretation and evaluation legendproviding rig (e.g., wireline) operation recommendations and solutions.

In some implementations, the wellbore operation includes adjustingand/or modifying a wireline operation. For example, the value of thefluid answer product prediction may facilitate in guiding the wirelineoperation as it moves downward through the region of interest. In otherimplementations, the wellbore operation includes adjusting and/ormodifying a production parameter. The wellbore operation is facilitatedby the value of the fluid answer product prediction to improve theservice quality of regional formation sampling and testing. In someaspects, the value of the fluid answer product prediction facilitatesthe wellbore operation to maximize the chance of quality data predictionfor the sensor deployed in future jobs.

FIG. 5 illustrates a flowchart diagram of an example of a sequentialprocess 500 for performing optical sensor adaptive calibration inaccordance with one or more implementations. Further for explanatorypurposes, the blocks of the sequential process 500 are described hereinas occurring in serial, or linearly. However, multiple blocks of theprocess 500 may occur in parallel. In addition, the blocks of theprocess 500 need not be performed in the order shown and/or one or moreof the blocks of the process 500 need not be performed.

In FIG. 5, the process 500 starts at step 502, where the subject systemoptimizes synthetic sensor data selection and processes from a globaloil and gas database. For example, the diversity of training dataselection and synthetic sensor response simulation are optimized tominimize the difference between simulation results and actual measuredsensor responses.

Next, at step 504, the subject system unitizes actual and syntheticsensor data scaling on selected reference fluids. For example, the scaleof measured actual sensor data and simulated synthetic sensor data areunitized on the selected reference fluids to improve data correlation.In some aspects, the sensor data is extracted from a correspondingsensor response, where the sensor response is a function of channelindex. In some aspects, the reference fluids typically consist ofrepresentative samples of dead oil, live oil, nature gas, water andnitrogen and other easily-obtainable non-petroleum fluids to manipulatedata distribution.

Subsequently, at step 506, training subsets are expanded with datanormalization by normalizing additional synthetic and actual sensor datato the dynamic data range of the given reference fluids. In someaspects, the normalized synthetic sensor data, for example, may exceedthe dynamic data range of the reference fluids. As such, training onout-of-range synthetic sensor data may help moderate prediction withactual sensor data on new fluids.

Next, at step 508, the synthetic and actual sensor data are combined toform a pooled calibration database. In some aspects, the syntheticsensor data distribution in adaptive calibration would mimic actualsensor data variation in potential applications, thus making futurereal-time data processing more robust.

Subsequently, at step 510, one or more neural networks are used to buildfluid predictive models based on the pooled calibration database.Although standard multi-layer MISO (multi-inputs and single-output)feedforward neural network and ensemble networks can be deployed inadaptive calibration to predict each fluid answer product, the increasedcomplexity of member network structure may be employed to improvecalibration accuracy with merged data in synthetic and actual sensorspaces.

FIG. 6 illustrates a waveform 600 depicting exemplary inputs for theoptical sensor adaptive calibration of FIG. 5 in accordance with one ormore implementations. The waveform 600 includes synthetic sensor datainputs superimposed with actual sensor data inputs on a normalized scalewith dynamic range on calibration inputs varied from about −4 to about+3.

FIG. 7 illustrates a flowchart diagram of an example of a process 700for optimizing real-time data processing in accordance with one or moreimplementations. Further for explanatory purposes, the blocks of thesequential process 700 are described herein as occurring in serial, orlinearly. However, multiple blocks of the process 700 may occur inparallel. In addition, the blocks of the process 700 need not beperformed in the order shown and/or one or more of the blocks of theprocess 700 need not be performed.

In FIG. 7, the process 700 applies the fluid answer product predictivemodels generated from adaptive calibration during a field job offormation sampling and testing for either a single pumpout scenario ormulti-pumpout scenarios. The process 700 starts at step 702, where anevaluation point is selected to monitor a neural network (NN) predictionduring pumpout with both raw and transformed sensor inputs.

Next, at step 704, a real-time quality analysis is performed todetermine which type of inputs generalizes better. Subsequently, at step706, the less robust signal processing routine is bypassed and the morerobust signal processing routine is used for the rest of the pumpoutscenario. Next, at step 708, a default setting of input type selectionis changed for other pumpout data predictions.

FIGS. 8A to 8D illustrate waveforms depicting respective comparisons ofresults of a real-world data prediction example from direct mappingwithout using optical data transformation in accordance with one or moreimplementations of the subject technology.

Each of the waveforms in FIGS. 8A to 8D compares the results of areal-world data prediction example from direct mapping (without usingconventional optical data transformation) as described in thisdisclosure and through actual sensor to synthetic sensor datatransformation in current standard practice. Note that a first curve(e.g., directed to a prediction from direct mapping) and a second curve(e.g., directed to a prediction through data transformation) in eachplot (e.g., 810, 820, 830, 840) are produced with same models from theadaptive calibration using actual and synthetic sensor inputsrespectively.

It can be observed from the plots (e.g., 810, 820, 830, 840) that theadaptive calibration methodology facilitates in smoothing thepredictions depicted by the second curve. The difference between thesecond curve and the first curve predictions can be explained by extraerror that may be introduced by additional data transformation(functional filter) of the second curves due to the use of a limitednumber of reference fluids in the transformation algorithm development.In comparison, the output curves from direct mapping are alsoself-consistent in predicted C1 concentration (e.g., 810), live fluiddensity (e.g., 820), C6+ concentration such as approximated sum ofsaturates, aromatics, resins and asphaltenes concentrations (e.g., 830),and GOR (e.g., 840), where the data quality may also be sufficient forearly decision making. The availability of both records makes futureoutput correction convenient when lab results on field samples becomeavailable, including evaluation of transformation error from the secondcurve predictions (i.e., through data transformation) and estimate ofthe bias from the first curve (i.e., from direct mapping) associatedwith actual sensor training data distribution.

FIG. 9 illustrates a flowchart diagram of an example of a process 900for performing sensor-and-field-based neural network calibration adaptedto both actual and synthetic sensor data. Further for explanatorypurposes, the blocks of the sequential process 900 are described hereinas occurring in serial, or linearly. However, multiple blocks of theprocess 900 may occur in parallel. In addition, the blocks of theprocess 900 need not be performed in the order shown and/or one or moreof the blocks of the process 900 need not be performed.

In FIG. 9, the process 900 summarizes the steps of alternativeimplementations that may be field (geological or geographical region)specific. When a particular optical sensor is used locally multipletimes, and lab results or reliable QC results on fluid samples of aprevious job become available, it is sensible to update the fluidpredictive model calibration and make it adaptive to local environmentalchange. Note that data integration is applied to the actual sensor spacewith the same normalization scheme discussed in FIG. 5. To optimize thenew field sensor data and existing calibration data distribution,performance evaluation of current models on field data using bothsynthetic and actual sensor inputs is applied. The job relevant sensordata can be used alone or integrated with existing calibration data tobuild new adaptive fluid models.

The process 900 starts at step 902, where operational sensor data isselected from field measurements when target fluid results areavailable. Next, at step 904, a current model prediction on field datais evaluated with both actual and synthetic sensor inputs. Subsequently,at step 906, a new field sensor is balanced with an existing calibrationdata distribution. Next, at step 908, field adaptive models are built onan integrated database with neural networks.

FIG. 10 illustrates an implementation of a general transformation modelframework including a forward transformation 1005 and a reversetransformation 1003 between data in a tool parameter space 1001 and asynthetic parameter space 1002 with a non-linear algorithm. In someimplementations, the non-linear algorithm used to implement both thereverse transformation 1003 and/or the forward transformation 1005 isthe NN algorithm. In some implementations, the forward 1005 or reverse1003 transformation includes a multi-input, multi-output neural networkthat may be applied by the fluid analysis device 134 of FIG. 1 toreceive inputs and generate outputs of sensing element responses. Themodel that converts the actual sensing element responses (SW/Ch01-Ch0n)from tool parameter space 1001 to synthetic parameter space 1002(PVT/Ch01-Ch0n) is referred to as reverse transformation 1003. The modelthat converts data from synthetic parameter space 1002 into toolparameter space 1001 is referred to as forward transformation 1005.Although the illustrated general transformation model framework in FIG.10 is configured with multi-input/multi-output non-linear neuralnetworks, there is no limitation in using other non-linear and lineartransformation algorithms with single-input/single-output andmulti-input/single-output configurations.

In some implementations, the NN algorithm may be deployed to obtain afluid characteristic using an ICE response and environmental factorssuch as a fluid temperature, a fluid pressure, and a fluid density(hereinafter collectively referred to as material factors). The NNalgorithm may include one or more hidden layers with each of the hiddennodes implemented with a nonlinear transfer function (e.g., hyperbolictangent sigmoid, or logarithmic sigmoid). The net input of each hiddennode on a first hidden layer is a weighted linear combination of allcalibration inputs according to the node connection. After the net inputis received at each hidden node, the NN algorithm calculates net outputusing the equipped nonlinear transfer function. The net output of firsthidden layer at each node may then be fed as input into nodes on asecond hidden layer (not shown). The single node on an output layer canbe implemented with either a linear transfer function or nonlineartransfer function. In some implementations, output node fluidcharacteristic may be a weighted linear combination, receiving inputsfrom outputs of the second hidden layer.

FIG. 11A depicts a schematic view of a logging operation deployed in andaround a well system 1100 a in accordance with one or moreimplementations. The well system 1100 a includes a logging system 1108and a subterranean region 1120 beneath the ground surface 1106. The wellsystem 1100 a can also include additional or different features that arenot shown in FIG. 11A. For example, the well system 1100 a can includeadditional drilling system components, wireline logging systemcomponents, or other components.

The subterranean region 1120 includes all or part of one or moresubterranean formations or zones. The subterranean region 1120 shown inFIG. 11A, for example, includes multiple subsurface layers 1122. Thesubsurface layers 1122 can include sedimentary layers, rock layers, sandlayers, or any combination thereof and other types of subsurface layers.One or more of the subsurface layers can contain fluids, such as brine,oil, gas, or combinations thereof. A wellbore 1104 penetrates throughthe subsurface layers 1122. Although the wellbore 1104 shown in FIG. 11Ais a vertical wellbore, the logging system 1108 can also be implementedin other wellbore orientations. For example, the logging system 1108 maybe adapted for horizontal wellbores, slant wellbores, curved wellbores,vertical wellbores, or any combination thereof.

The logging system 1108 also includes a logging tool 1102, surfaceequipment 1112, and a computing subsystem 1110. In the shown in FIG.11A, the logging tool 1102 is a downhole logging tool that operateswhile disposed in the wellbore 1104. The surface equipment 1112 shown inFIG. 11A operates at or above the surface 1106, for example, near thewell head 1105, to control the logging tool 1102 and possibly otherdownhole equipment or other components of the well system 1100 a. Thecomputing subsystem 1110 receives and analyzes logging data from alogging tool 1102. A logging system can include additional or differentfeatures, and the features of a logging system can be arranged andoperated as represented in FIG. 11A or in another manner.

All or part of the computing subsystem 1110 can be implemented as acomponent of, or integrated with one or more components of, the surfaceequipment 1112, the logging tool 1102, or both. For example, thecomputing subsystem 1110 can be implemented as one or more computingstructures separate from but communicative with the surface equipment1112 and the logging tool 1102.

The computing subsystem 1110 can be embedded in the logging tool 1102(not shown), and the computing subsystem 1110 and the logging tool 1102operate concurrently while disposed in the wellbore 1104. For example,although the computing subsystem 1110 is shown above the surface 1106 inFIG. 11A, all or part of the computing subsystem 1110 may reside belowthe surface 1106, for example, at or near the location of the loggingtool 1102.

The well system 1100 a includes communication or telemetry equipmentthat allows communication among the computing subsystem 1110, thelogging tool 1102, and other components of the logging system 1108. Forexample, each of the components of the logging system 1108 can includeone or more transceivers or similar apparatus for wired or wireless datacommunication among the various components. The logging system 1108 caninclude, but is not limited to, one or more systems and/or apparatus forwireline telemetry, wired pipe telemetry, mud pulse telemetry, acoustictelemetry, electromagnetic telemetry, or any combination of these andother types of telemetry. In some implementations, the logging tool 1102receives commands, status signals, or other types of information fromthe computing subsystem 1110 or another source. The computing subsystem1110 can also receive logging data, status signals, or other types ofinformation from the logging tool 1102 or another source.

Logging operations are performed in connection with various types ofdownhole operations at various stages in the lifetime of a well systemand therefore structural attributes and components of the surfaceequipment 1112 and logging tool 1102 are adapted for various types oflogging operations. For example, logging may be performed duringdrilling operations, during wireline logging operations, or in othercontexts. As such, the surface equipment 1112 and the logging tool 1102can include or operate in connection with drilling equipment, wirelinelogging equipment, or other equipment for other types of operations.

FIG. 11B depicts a schematic view of a wireline logging operationdeployed in and around a well system 1100 b in accordance with one ormore implementations. The well system 1100 b includes the logging tool1102 in a wireline logging environment. The surface equipment 1112includes, but is not limited to, a platform 1101 disposed above thesurface 1106 equipped with a derrick 1132 that supports a wireline cable1134 extending into the wellbore 1104. Wireline logging operations areperformed, for example, after a drill string is removed from thewellbore 1104, to allow the wireline logging tool 1102 to be lowered bywireline or logging cable into the wellbore 1104.

FIG. 11C depicts a schematic view of a well system 1100 c that includesthe logging tool 1102 in a logging while drilling (LWD) environment inaccordance with one or more implementations. In some implementations,the LWD environment includes logging operations being performed duringdrilling operations. Drilling is performed using a string of drill pipesconnected together to form a drill string 1140 that is lowered through arotary table into the wellbore 1104. A drilling rig 1142 at the surface1106 supports the drill string 1140, as the drill string 1140 isoperated to drill a wellbore penetrating the subterranean region 1120.The drill string 1140 can include, for example, but is not limited to, akelly, a drill pipe, a bottom hole assembly, and other components. Thebottomhole assembly on the drill string can include drill collars, drillbits, the logging tool 1102, and other components. Exemplary loggingtools can be or include, but are not limited to, measuring whiledrilling (MWD) tools and LWD tools.

The logging tool 1102 includes a tool for obtaining measurements fromthe subterranean region 1120. As shown, for example, in FIG. 11B, thelogging tool 1102 is suspended in the wellbore 1104 by a coiled tubing,wireline cable, or another structure or conveyance that connects thetool to a surface control unit or other components of the surfaceequipment 1112.

The logging tool 1102 is lowered to the bottom of a region of interestand subsequently pulled upward (e.g., at a substantially constant speed)through the region of interest. As shown, for example, in FIG. 11C, thelogging tool 1102 is deployed in the wellbore 1104 on jointed drillpipe, hard wired drill pipe, or other deployment hardware. In otherexample implementations, the logging tool 1102 collects data duringdrilling operations as it moves downward through the region of interest.The logging tool 1102 may also collect data while the drill string 1140is moving, for example, while the logging tool 1102 is being tripped inor tripped out of the wellbore 1104.

The logging tool 1102 may also collect data at discrete logging pointsin the wellbore 1104. For example, the logging tool 1102 moves upward ordownward incrementally to each logging point at a series of depths inthe wellbore 1104. At each logging point, instruments in the loggingtool 1102 perform measurements on the subterranean region 1120. Thelogging tool 1102 also obtains measurements while the logging tool 1102is moving (e.g., being raised or lowered). The measurement data iscommunicated to the computing subsystem 1110 for storage, processing,and analysis. Such data may be gathered and analyzed during drillingoperations (e.g., LWD operations), during wireline logging operations,other conveyance operations, or during other types of activities.

The computing subsystem 1110 receives and analyzes the measurement datafrom the logging tool 1102 to detect properties of various subsurfacelayers 1122. For example, the computing subsystem 1110 can identify thedensity, material content, and/or other properties of the subsurfacelayers 1122 based on the measurements acquired by the logging tool 1102in the wellbore 1104.

FIG. 12 is a block diagram illustrating an exemplary computer system1200 with which the computing subsystem 1110 of FIG. 11A can beimplemented. In certain aspects, the computer system 1200 may beimplemented using hardware or a combination of software and hardware,either in a dedicated server, integrated into another entity, ordistributed across multiple entities.

Computer system 1200 (e.g., computing subsystem 1110) includes a bus1208 or other communication mechanism for communicating information, anda processor 1202 coupled with bus 1208 for processing information. Byway of example, the computer system 1200 may be implemented with one ormore processors 1202. Processor 1202 may be a general-purposemicroprocessor, a microcontroller, a Digital Signal Processor (DSP), anApplication Specific Integrated Circuit (ASIC), a Field ProgrammableGate Array (FPGA), a Programmable Logic Device (PLD), a controller, astate machine, gated logic, discrete hardware components, or any othersuitable entity that can perform calculations or other manipulations ofinformation.

Computer system 1200 can include, in addition to hardware, code thatcreates an execution environment for the computer program in question,e.g., code that constitutes processor firmware, a protocol stack, adatabase management system, an operating system, or a combination of oneor more of them stored in an included memory 1204, such as a RandomAccess Memory (RAM), a flash memory, a Read Only Memory (ROM), aProgrammable Read-Only Memory (PROM), an Erasable PROM (EPROM),registers, a hard disk, a removable disk, a CD-ROM, a DVD, or any othersuitable storage device, coupled to bus 1208 for storing information andinstructions to be executed by processor 1202. The processor 1202 andthe memory 1204 can be supplemented by, or incorporated in, specialpurpose logic circuitry.

The instructions may be stored in the memory 1204 and implemented in oneor more computer program products, i.e., one or more modules of computerprogram instructions encoded on a computer readable medium for executionby, or to control the operation of, the computer system 1200, andaccording to any method well known to those of skill in the art,including, but not limited to, computer languages such as data-orientedlanguages (e.g., SQL, dBase), system languages (e.g., C, Objective-C,C++, Assembly), architectural languages (e.g., Java, .NET), andapplication languages (e.g., PHP, Ruby, Perl, Python). Instructions mayalso be implemented in computer languages such as array languages,aspect-oriented languages, assembly languages, authoring languages,command line interface languages, compiled languages, concurrentlanguages, curly-bracket languages, dataflow languages, data-structuredlanguages, declarative languages, esoteric languages, extensionlanguages, fourth-generation languages, functional languages,interactive mode languages, interpreted languages, iterative languages,list-based languages, little languages, logic-based languages, machinelanguages, macro languages, metaprogramming languages, multiparadigmlanguages, numerical analysis, non-English-based languages,object-oriented class-based languages, object-oriented prototype-basedlanguages, off-side rule languages, procedural languages, reflectivelanguages, rule-based languages, scripting languages, stack-basedlanguages, synchronous languages, syntax handling languages, visuallanguages, with languages, and xml-based languages. Memory 1204 may alsobe used for storing temporary variable or other intermediate informationduring execution of instructions to be executed by processor 1202.

A computer program as discussed herein does not necessarily correspondto a file in a file system. A program can be stored in a portion of afile that holds other programs or data (e.g., one or more scripts storedin a markup language document), in a single file dedicated to theprogram in question, or in multiple coordinated files (e.g., files thatstore one or more modules, subprograms, or portions of code). A computerprogram can be deployed to be executed on one computer or on multiplecomputers that are located at one site or distributed across multiplesites and interconnected by a communication network. The processes andlogic flows described in this specification can be performed by one ormore programmable processors executing one or more computer programs toperform functions by operating on input data and generating output.

Computer system 1200 further includes a data storage device 1206 such asa magnetic disk or optical disk, coupled to bus 1208 for storinginformation and instructions. Computer system 1200 may be coupled viainput/output module 1210 to various devices. The input/output module1210 can be any input/output module. Exemplary input/output modules 1210include data ports such as USB ports. The input/output module 1210 isconfigured to connect to a communications module 1212. Exemplarycommunications modules 1212 include networking interface cards, such asEthernet cards and modems. In certain aspects, the input/output module1210 is configured to connect to a plurality of devices, such as aninput device 1214 and/or an output device 1216. Exemplary input devices1214 include a keyboard and a pointing device, e.g., a mouse or atrackball, by which a user can provide input to the computer system1200. Other kinds of input devices 1214 can be used to provide forinteraction with a user as well, such as a tactile input device, visualinput device, audio input device, or brain-computer interface device.For example, feedback provided to the user can be any form of sensoryfeedback, e.g., visual feedback, auditory feedback, or tactile feedback,and input from the user can be received in any form, including acoustic,speech, tactile, or brain wave input. Exemplary output devices 1216include display devices such as an LCD (liquid crystal display) monitor,for displaying information to the user, or diagnostic devices such as anoscilloscope.

According to one aspect of the present disclosure, the computingsubsystem 110 can be implemented using a computer system 1200 inresponse to processor 1202 executing one or more sequences of one ormore instructions contained in memory 1204. Such instructions may beread into memory 1204 from another machine-readable medium, such as datastorage device 1206. Execution of the sequences of instructionscontained in the main memory 1204 causes processor 1202 to perform theprocess steps described herein. One or more processors in amulti-processing arrangement may also be employed to execute thesequences of instructions contained in the memory 1204. In alternativeaspects, hard-wired circuitry may be used in place of or in combinationwith software instructions to implement various aspects of the presentdisclosure. Thus, aspects of the present disclosure are not limited toany specific combination of hardware circuitry and software.

Various aspects of the subject matter described in this specificationcan be implemented in a computing system that includes a back endcomponent, e.g., such as a data server, or that includes a middlewarecomponent, e.g., an application server, or that includes a front endcomponent, e.g., a client computer having a graphical user interface ora Web browser through which a user can interact with an implementationof the subject matter described in this specification, or anycombination of one or more such back end, middleware, or front endcomponents. The components of the system can be interconnected by anyform or medium of digital data communication, e.g., a communicationnetwork. The communication network can include, for example, any one ormore of a LAN, a WAN, the Internet, and the like. Further, thecommunication network can include, but is not limited to, for example,any one or more of the following network topologies, including a busnetwork, a star network, a ring network, a mesh network, a star-busnetwork, tree or hierarchical network, or the like. The communicationsmodules can be, for example, modems or Ethernet cards.

Computer system 1200 can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.Computer system 1200 can be, for example, and without limitation, adesktop computer, laptop computer, or tablet computer. Computer system1200 can also be embedded in another device, for example, and withoutlimitation, a mobile telephone such as a smartphone.

The term “machine-readable storage medium” or “computer readable medium”as used herein refers to any medium or media that participates inproviding instructions to processor 1202 for execution. Such a mediummay take many forms, including, but not limited to, non-volatile media,volatile media, and transmission media. Non-volatile media include, forexample, optical or magnetic disks, such as data storage device 1206.Volatile media include dynamic memory, such as memory 1204. Transmissionmedia include coaxial cables, copper wire, and fiber optics, includingthe wires that comprise bus 1208. Common forms of machine-readable mediainclude, for example, floppy disk, a flexible disk, hard disk, magnetictape, any other magnetic medium, a CD-ROM, DVD, any other opticalmedium, punch cards, paper tape, any other physical medium with patternsof holes, a RAM, a PROM, an EPROM, a FLASH EPROM, any other memory chipor cartridge, or any other medium from which a computer can read. Themachine-readable storage medium can be a machine-readable storagedevice, a machine-readable storage substrate, a memory device, acomposition of matter effecting a machine-readable propagated signal, ora combination of one or more of them.

Various examples of aspects of the disclosure are described below. Theseare provided as examples, and do not limit the subject technology.

According to one embodiment of the subject technology, a method includesdeploying an optical tool into a wellbore penetrating a subterraneanformation; obtaining field measurements with the deployed optical tool;determining an adaptive fluid predictive model calibrated with aplurality of types of sensor data, the plurality of types of sensorresponses comprising a first type of sensor response associated with asynthetic parameter space and a second type of sensor responseassociated with a tool parameter space; applying, in a processorcircuit, the adaptive fluid predictive model to one or more fluidsamples from the obtained field measurements; determining a value of afluid answer product prediction with the applied adaptive fluidpredictive model; and providing a fluid answer signal indicating thevalue of the fluid answer product prediction for facilitating downholefluid sampling with a wellbore operation.

According to one embodiment of the subject technology, a system includesa downhole tool; and a fluid analysis device operably coupled to thedownhole tool and having a memory and a processor, wherein the memorycomprises commands which, when executed by the processor, cause thefluid analysis device to select at least one of a plurality ofevaluation points in a fluid predictive model to monitor the fluidpredictive model during a pumpout operation with a plurality of types ofsensor data inputs, the plurality of types of sensor data inputscomprising synthetic sensor responses and actual sensor responses;apply, in a processing circuit, the fluid predictive model to theplurality of types of sensor data inputs the selected evaluation point;determine which type of the plurality of types of sensor data inputsproduces a more robust signal output with the applied fluid predictivemodel within the selected at least one of the plurality of evaluationpoints; bypass type of sensor data that produces a less robust signaloutput with the fluid predictive model; apply the type of sensor datathat produces the more robust signal output with the fluid predictivemodel for a remainder duration of the pumpout operation; and modify asetting of input type selection to the fluid predictive model with theapplied type of sensor data for facilitating other pumpout dataprediction associated with a wellbore operation.

According to one embodiment of the subject technology, a non-transitorycomputer-readable medium storing instructions which, when executed by aprocessor, cause a computer to obtain available target fluid results onfluid samples of a prior field measurement; select a type of sensor datafrom field measurements associated with the available target fluidresults; determine an adaptive fluid predictive model calibrated with aplurality of types of sensor inputs; apply, in a processing circuit, theadaptive fluid predictive model to the selected type of sensor data;process a signal output of the adaptive fluid predictive model;determine whether the signal output associated with the selected type ofsensor data does not exceed a predetermined output correction threshold;integrate the selected type of sensor data to an existing calibrationdata distribution with a normalization algorithm to form a modifiedcalibration data distribution when the signal output is determined notto exceed the predetermined output correction threshold; and generate amodified adaptive fluid predictive model using the modified calibrationdata distribution with neural networks.

In one or more aspects, examples of clauses are described below.

A method comprising one or more methods, operations or portions thereofdescribed herein.

An apparatus comprising one or more memories and one or more processors(e.g., 1200), the one or more processors configured to cause performingone or more methods, operations or portions thereof described herein.

An apparatus comprising one or more memories (e.g., 1204, one or moreinternal, external or remote memories, or one or more registers) and oneor more processors (e.g., 1202) coupled to the one or more memories, theone or more processors configured to cause the apparatus to perform oneor more methods, operations or portions thereof described herein.

An apparatus comprising means (e.g., 1200) adapted for performing one ormore methods, operations or portions thereof described herein.

A processor (e.g., 1202) comprising modules for carrying out one or moremethods, operations or portions thereof described herein.

A hardware apparatus comprising circuits (e.g., 1200) configured toperform one or more methods, operations or portions thereof describedherein.

An apparatus comprising means (e.g., 1200) adapted for performing one ormore methods, operations or portions thereof described herein.

An apparatus comprising components (e.g., 1200) operable to carry outone or more methods, operations or portions thereof described herein.

A computer-readable storage medium (e.g., 1204, one or more internal,external or remote memories, or one or more registers) comprisinginstructions stored therein, the instructions comprising code forperforming one or more methods or operations described herein.

A computer-readable storage medium (e.g., 1204, one or more internal,external or remote memories, or one or more registers) storinginstructions that, when executed by one or more processors, cause one ormore processors to perform one or more methods, operations or portionsthereof described herein.

In one aspect, a method may be an operation, an instruction, or afunction and vice versa. In one aspect, a clause or a claim may beamended to include some or all of the words (e.g., instructions,operations, functions, or components) recited in other one or moreclauses, one or more words, one or more sentences, one or more phrases,one or more paragraphs, and/or one or more claims.

To illustrate the interchangeability of hardware and software, itemssuch as the various illustrative blocks, modules, components, methods,operations, instructions, and algorithms have been described generallyin terms of their functionality. Whether such functionality isimplemented as hardware, software or a combination of hardware andsoftware depends upon the particular application and design constraintsimposed on the overall system. Skilled artisans may implement thedescribed functionality in varying ways for each particular application.

A reference to an element in the singular is not intended to mean oneand only one unless specifically so stated, but rather one or more. Forexample, “a” module may refer to one or more modules. An elementproceeded by “a,” “an,” “the,” or “said” does not, without furtherconstraints, preclude the existence of additional same elements.

Headings and subheadings, if any, are used for convenience only and donot limit the subject technology. The word exemplary is used to meanserving as an example or illustration. To the extent that the terminclude, have, or the like is used, such term is intended to beinclusive in a manner similar to the term comprise as comprise isinterpreted when employed as a transitional word in a claim. Relationalterms such as first and second and the like may be used to distinguishone entity or action from another without necessarily requiring orimplying any actual such relationship or order between such entities oractions.

Phrases such as an aspect, the aspect, another aspect, some aspects, oneor more aspects, an implementation, the implementation, anotherimplementation, some implementations, one or more implementations, anembodiment, the embodiment, another embodiment, some embodiments, one ormore embodiments, a configuration, the configuration, anotherconfiguration, some configurations, one or more configurations, thesubject technology, the disclosure, the present disclosure, othervariations thereof and alike are for convenience and do not imply that adisclosure relating to such phrase(s) is essential to the subjecttechnology or that such disclosure applies to all configurations of thesubject technology. A disclosure relating to such phrase(s) may apply toall configurations, or one or more configurations. A disclosure relatingto such phrase(s) may provide one or more examples. A phrase such as anaspect or some aspects may refer to one or more aspects and vice versa,and this applies similarly to other foregoing phrases.

A phrase “at least one of” preceding a series of items, with the terms“and” or “or” to separate any of the items, modifies the list as awhole, rather than each member of the list. The phrase “at least one of”does not require selection of at least one item; rather, the phraseallows a meaning that includes at least one of any one of the items,and/or at least one of any combination of the items, and/or at least oneof each of the items. By way of example, each of the phrases “at leastone of A, B, and C” or “at least one of A, B, or C” refers to only A,only B, or only C; any combination of A, B, and C; and/or at least oneof each of A, B, and C.

It is understood that the specific order or hierarchy of steps,operations, or processes disclosed is an illustration of exemplaryapproaches. Unless explicitly stated otherwise, it is understood thatthe specific order or hierarchy of steps, operations, or processes maybe performed in different order. Some of the steps, operations, orprocesses may be performed simultaneously. The accompanying methodclaims, if any, present elements of the various steps, operations orprocesses in a sample order, and are not meant to be limited to thespecific order or hierarchy presented. These may be performed in serial,linearly, in parallel or in different order. It should be understoodthat the described instructions, operations, and systems can generallybe integrated together in a single software/hardware product or packagedinto multiple software/hardware products.

The disclosure is provided to enable any person skilled in the art topractice the various aspects described herein. In some instances,well-known structures and components are shown in block diagram form inorder to avoid obscuring the concepts of the subject technology. Thedisclosure provides various examples of the subject technology, and thesubject technology is not limited to these examples. Variousmodifications to these aspects will be readily apparent to those skilledin the art, and the principles described herein may be applied to otheraspects.

All structural and functional equivalents to the elements of the variousaspects described throughout the disclosure that are known or later cometo be known to those of ordinary skill in the art are expresslyincorporated herein by reference and are intended to be encompassed bythe claims. Moreover, nothing disclosed herein is intended to bededicated to the public regardless of whether such disclosure isexplicitly recited in the claims. No claim element is to be construedunder the provisions of 35 U.S.C. § 112, sixth paragraph, unless theelement is expressly recited using the phrase “means for” or, in thecase of a method claim, the element is recited using the phrase “stepfor”.

The title, background, brief description of the drawings, abstract, anddrawings are hereby incorporated into the disclosure and are provided asillustrative examples of the disclosure, not as restrictivedescriptions. It is submitted with the understanding that they will notbe used to limit the scope or meaning of the claims. In addition, in thedetailed description, it can be seen that the description providesillustrative examples and the various features are grouped together invarious implementations for the purpose of streamlining the disclosure.The method of disclosure is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, as the claims reflect,inventive subject matter lies in less than all features of a singledisclosed configuration or operation. The claims are hereby incorporatedinto the detailed description, with each claim standing on its own as aseparately claimed subject matter.

The claims are not intended to be limited to the aspects describedherein, but are to be accorded the full scope consistent with thelanguage claims and to encompass all legal equivalents. Notwithstanding,none of the claims are intended to embrace subject matter that fails tosatisfy the requirements of the applicable patent law, nor should theybe interpreted in such a way.

Therefore, the subject technology is well adapted to attain the ends andadvantages mentioned as well as those that are inherent therein. Theparticular embodiments disclosed above are illustrative only, as thesubject technology may be modified and practiced in different butequivalent manners apparent to those skilled in the art having thebenefit of the teachings herein. Furthermore, no limitations areintended to the details of construction or design herein shown, otherthan as described in the claims below. It is therefore evident that theparticular illustrative embodiments disclosed above may be altered,combined, or modified and all such variations are considered within thescope and spirit of the subject technology. The subject technologyillustratively disclosed herein suitably may be practiced in the absenceof any element that is not specifically disclosed herein and/or anyoptional element disclosed herein. While compositions and methods aredescribed in terms of “comprising,” “containing,” or “including” variouscomponents or steps, the compositions and methods can also “consistessentially of” or “consist of” the various components and steps. Allnumbers and ranges disclosed above may vary by some amount. Whenever anumerical range with a lower limit and an upper limit is disclosed, anynumber and any included range falling within the range is specificallydisclosed. In particular, every range of values (of the form, “fromabout a to about b,” or, equivalently, “from approximately a to b.” or,equivalently, “from approximately a-b”) disclosed herein is to beunderstood to set forth every number and range encompassed within thebroader range of values. Also, the terms in the claims have their plain,ordinary meaning unless otherwise explicitly and clearly defined by thepatentee. Moreover, the indefinite articles “a” or “an,” as used in theclaims, are defined herein to mean one or more than one of the elementthat it introduces. If there is any conflict in the usages of a word orterm in this specification and one or more patent or other documentsthat may be incorporated herein by reference, the definitions that areconsistent with this specification should be adopted.

What is claimed is:
 1. A method, comprising: deploying an optical toolinto a wellbore penetrating a subterranean formation; obtaining fieldmeasurements with the deployed optical tool; determining an adaptivefluid predictive model calibrated with a plurality of types of sensordata, the plurality of types of sensor responses comprising a first typeof sensor response associated with a synthetic parameter space and asecond type of sensor response associated with a tool parameter space;applying, in a processor circuit, the adaptive fluid predictive model toone or more fluid samples from the obtained field measurements;determining a value of a fluid answer product prediction with theapplied adaptive fluid predictive model; and providing a fluid answersignal indicating the value of the fluid answer product prediction forfacilitating downhole fluid sampling with a wellbore operation.
 2. Themethod of claim 1, wherein determining the adaptive fluid predictivemodel comprises: calibrating fluid answer product predictive models forreal-time downhole fluid analysis during formation fluid sampling andtesting using synthetic sensor data of the first type of sensor responseand actual sensor data of the second type of sensor response withcorresponding target fluid characteristics.
 3. The method of claim 1,further comprising: calibrating a transformation algorithm on a selectednumber of reference fluids with measured actual sensor responses andsynthetic sensor responses at same temperature and pressure settingpoints.
 4. The method of claim 3, wherein the transformation algorithmis calibrated using a training data distribution that includes an actualsensor data distribution and a synthetic sensor data distribution. 5.The method of claim 4, wherein the training data distribution representsfluid information of a plurality of reference fluids in differentgeographical regions.
 6. The method of claim 3, further comprising:converting the second type of sensor response to the first type ofsensor response through a nonlinear mapping using the transformationalgorithm with one or more neural networks.
 7. The method of claim 1,further comprising: synthesizing the first type of sensor response fromone or more of optical fluid spectroscopy measurements, measured sensorwheel transmittance spectra, and transfer function of an optical trainin the optical tool.
 8. The method of claim 1, wherein determining theadaptive fluid predictive model comprises: combining the second type ofsensor response with the first type of sensor response through anormalization algorithm to create an integrated data calibrationdistribution.
 9. The method of claim 1, further comprising: obtainingprior operational sensor data; modifying a calibration of a fluidpredictive model with the obtained prior operational sensor data; andproviding a regional historical fluid data interpretation with themodified calibration of the fluid predictive model.
 10. The method ofclaim 9, wherein the calibration of the fluid predictive model ismodified with the prior operational sensor data and a calibration datadistribution that includes actual and synthetic sensor data.
 11. Themethod of claim 10, wherein the prior operational sensor data isobtained with the optical tool in a region of the subterranean formationthat corresponds to that of the calibration data distribution.
 12. Asystem, comprising: a downhole tool; and a fluid analysis deviceoperably coupled to the downhole tool and having a memory and aprocessor, wherein the memory comprises commands which, when executed bythe processor, cause the fluid analysis device to: select at least oneof a plurality of evaluation points in a fluid predictive model tomonitor the fluid predictive model during a pumpout operation with aplurality of types of sensor data inputs, the plurality of types ofsensor data inputs comprising synthetic sensor responses and actualsensor responses; apply, in a processing circuit, the fluid predictivemodel to the plurality of types of sensor data inputs the selectedevaluation point; determine which type of the plurality of types ofsensor data inputs produces a more robust signal output with the appliedfluid predictive model within the selected at least one of the pluralityof evaluation points; bypass type of sensor data that produces a lessrobust signal output with the fluid predictive model; apply the type ofsensor data that produces the more robust signal output with the fluidpredictive model for a remainder duration of the pumpout operation; andmodify a setting of input type selection to the fluid predictive modelwith the applied type of sensor data for facilitating other pumpout dataprediction associated with a wellbore operation.
 13. The system of claim12, wherein the commands which, when executed by the processor, furthercause the system to: generate training data with a diverse selection ofthe actual sensor responses from a plurality of optical tool sources;and reduce a difference between the synthetic sensor responses and theactual sensor responses from the training data to optimize the syntheticsensor responses.
 14. The system of claim 13, wherein the commandswhich, when executed by the processor, further cause the system to:unitize a scale of the actual sensor responses and the synthetic sensorresponses on selected reference fluids to increase a data correlationbetween the actual sensor responses and the synthetic sensor responses;and normalize the unitized scale of the synthetic sensor responses andthe actual sensor responses with a dynamic range of the selectedreference fluids to form a normalized scale of the synthetic sensorresponses and the actual sensor responses.
 15. The system of claim 14,wherein the commands which, when executed by the processor, furthercause the system to: combine the synthetic sensor responses and theactual sensor responses from the normalized scale to form a pooledcalibration database with a plurality of calibration input patterns; andgenerate one or more adaptive fluid predictive models using theplurality of calibration input patterns from the pooled calibrationdatabase with neural networks.
 16. The system of claim 12, wherein thecommands which, when executed by the processor, further cause the systemto: recalibrate the fluid predictive model with prior field job data togenerate a reconstructed fluid predictive model.
 17. A non-transitorycomputer-readable medium storing instructions which, when executed by aprocessor, cause a computer to: obtain available target fluid results onfluid samples of a prior field measurement; select a type of sensor datafrom field measurements associated with the available target fluidresults; determine an adaptive fluid predictive model calibrated with aplurality of types of sensor inputs; apply, in a processing circuit, theadaptive fluid predictive model to the selected type of sensor data;process a signal output of the adaptive fluid predictive model;determine whether the signal output associated with the selected type ofsensor data does not exceed a predetermined output correction threshold;integrate the selected type of sensor data to an existing calibrationdata distribution with a normalization algorithm to form a modifiedcalibration data distribution when the signal output is determined notto exceed the predetermined output correction threshold; and generate amodified adaptive fluid predictive model using the modified calibrationdata distribution with neural networks.
 18. The non-transitorycomputer-readable medium of claim 17, wherein the instructions which,when executed by the processor, further cause the computer to:recalibrate the adaptive fluid predictive model with at least theselected type of sensor data for generating the modified adaptive fluidpredictive model.
 19. The non-transitory computer-readable medium ofclaim 17, wherein the instructions which, when executed by theprocessor, further cause the computer to: determine a fluidcharacteristic using the modified adaptive fluid predictive model withthe plurality of types of sensor inputs from the modified calibrationdata distribution.
 20. The non-transitory computer-readable medium ofclaim 19, wherein the plurality of types of sensor inputs comprise anactual sensor input and a synthetic sensor input.